Image-based 3D-printing for biomedical applications

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Motivation: There has been considerable recent development in the area of additive manufacturing ("3D-printing"), including the development of techniques for printing directly in metal alloys, using powder-bed laser fusion. Additive manufacturing in metal alloys has become routinely available to researchers and manufacturers, with commercial systems widely available. These techniques have increased the potential for precision medicine, including patient-specific components and surgical guides. For biomedical applications, additive manufacturing is driven by advanced imaging (CT and MRI), as the required geometric data is typically derived from 3D volume images. There is an opportunity for significant improvement in the fundamental processes by which images are converted into source data for component design. Similarly, 3D imaging can serve an important role in verifying and validating the shape and mechanical performance of fabricated components for biomedical applications. Objectives: In this program, we will explore the implementation of novel algorithms (including machine learning and deep learning) for image reconstruction and segmentation, with the goal of improving the efficiency and accuracy of biomedical 3D printing. We will also develop techniques for process monitoring, as part of next-generation quality management. The techniques that we develop will be applied to a range of research applications, including veterinary components, porous scaffolds, and "smart" implants. We believe that the development of an integrated "pipeline" for design and testing will significantly expand the impact of image-based 3D printing. Approach: Our research approach for this program is focused on three themes: (1) optimization of image acquisition and segmentation, using dual-energy imaging, iterative reconstruction, and model-based deep-learning; (2) Image-based finite-element design and optimization, validated with dynamic 3D non-destructive testing and embedded sensors; (3) image-based validation and process improvement, using quantitative 3D computed tomography and in-process optical imaging during laser powder-bed fusion. Impact: This program integrates several aspects of recently developed technology and new computing algorithms (such as deep learning using neural networks), creating a new platform of software tools for image-based design and fabrication in biomedical engineering. Anticipated advances from this program include new tools to extract geometry data from 3D image volumes, and new hardware-software techniques to verify FE models (i.e. mechanical properties, drug elution, and thermal transport). This project takes advantage of expertise in several areas of the natural sciences and engineering, including: imaging science, biomechanical engineering, advanced manufacturing, and software engineering. The proposed program will lead to novel software utilities and techniques with broad applications in research and development.